From e9bac094277fc3f06445d6e2bff7c810343289b9 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 31 Aug 2023 06:13:22 +0200 Subject: [PATCH] small typos --- doc/pub/week35/html/._week35-bs057.html | 2 +- doc/pub/week35/html/._week35-bs058.html | 2 +- doc/pub/week35/html/week35-reveal.html | 4 +- doc/pub/week35/html/week35-solarized.html | 4 +- doc/pub/week35/html/week35.html | 4 +- doc/pub/week35/ipynb/ipynb-week35-src.tar.gz | Bin 191 -> 191 bytes doc/pub/week35/ipynb/week35.ipynb | 860 +++++++++---------- doc/src/week35/week35.do.txt | 4 +- 8 files changed, 440 insertions(+), 440 deletions(-) diff --git a/doc/pub/week35/html/._week35-bs057.html b/doc/pub/week35/html/._week35-bs057.html index 5c4bde9f6..047b36e1e 100644 --- a/doc/pub/week35/html/._week35-bs057.html +++ b/doc/pub/week35/html/._week35-bs057.html @@ -377,7 +377,7 @@ function, that is we have

$$ -\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}^T\partial \boldsymbol{\beta}} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}. +\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}. $$

This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).

diff --git a/doc/pub/week35/html/._week35-bs058.html b/doc/pub/week35/html/._week35-bs058.html index f52640fb5..b6bab24dc 100644 --- a/doc/pub/week35/html/._week35-bs058.html +++ b/doc/pub/week35/html/._week35-bs058.html @@ -405,7 +405,7 @@ with a factor \( 1/(n-1) \). This is called Scikit-Learn or nunmpy's function calculate the covariance, this +Scikit-Learn or nunmpy's function to calculate the covariance, this quantity will be computed with a factor \( 1/(n-1) \).

diff --git a/doc/pub/week35/html/week35-solarized.html b/doc/pub/week35/html/week35-solarized.html index 43267a8e9..0f1fa52af 100644 --- a/doc/pub/week35/html/week35-solarized.html +++ b/doc/pub/week35/html/week35-solarized.html @@ -2823,7 +2823,7 @@ function, that is we have

$$ -\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}^T\partial \boldsymbol{\beta}} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}. +\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}. $$

This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).

@@ -2879,7 +2879,7 @@ with a factor \( 1/(n-1) \). This is called
Scikit-Learn or nunmpy's function calculate the covariance, this +Scikit-Learn or nunmpy's function to calculate the covariance, this quantity will be computed with a factor \( 1/(n-1) \).

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